1
Add the following code to your Python file
knowledge_tools.py
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIResponses
from agno.tools.knowledge import KnowledgeTools
from agno.vectordb.lancedb import LanceDb, SearchType
# Create a knowledge containing information from a URL
agno_docs = Knowledge(
# Use LanceDB as the vector database and store embeddings in the `agno_docs` table
vector_db=LanceDb(
uri="tmp/lancedb",
table_name="agno_docs",
search_type=SearchType.hybrid,
embedder=OpenAIEmbedder(id="text-embedding-3-small"),
),
)
# Add content to the knowledge
agno_docs.insert(url="https://docs.agno.com/llms-full.txt")
knowledge_tools = KnowledgeTools(
knowledge=agno_docs,
think=True,
search=True,
analyze=True,
add_few_shot=True,
)
agent = Agent(
model=OpenAIResponses(id="gpt-5.2"),
tools=[knowledge_tools],
markdown=True,
)
if __name__ == "__main__":
agent.print_response(
"How do I build a team of agents in agno?",
markdown=True,
stream=True,
stream_tools=True,
)
2
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
3
Install dependencies
uv pip install -U agno openai lancedb
4
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
5
Run Agent
python knowledge_tools.py